Threshold Estimation Models for Influence Maximization in Social Network

홈 > 연구문헌 > 학술대회 프로시딩 > 한국정보과학회 학술대회 > 2016년 동계학술발표회

한글제목(Korean Title)	Threshold Estimation Models for Influence Maximization in Social Network
영문제목(English Title)	Threshold Estimation Models for Influence Maximization in Social Network
저자(Author)	Ashis Talukder Md. Golam Rabiul Alam Anupam Kumar Bairagi Sarder Fakrul Abedin Hoang T. Nguyen Choong Seon Hong
원문수록처(Citation)	VOL 43 NO. 02 PP. 0888 ~ 0890 (2016. 12)
한글내용 (Korean Abstract)
영문내용 (English Abstract)	The Influence Maximization (IM) problem determines a small set of influential users that maximizes the influence spread in the network where influence is measured by the number of active nodes. Among the two classical models, in the Linear Threshold (LT) a node is activated if the total influence of all the active in-neighbors is no less than a given threshold and thus threshold selection is important. In this research we observe that the threshold depends on the application of IM and the influence weight. Then we propose different threshold models based on influence weight. Our models are linear and the simulation on real dataset shows that they have fast running time.
키워드(Keyword)
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